We compare our AMPL with two DICE methods — AlgaeDICE [1]} and OptiDICE [2]} —
that directly utilize MIW to improve policy learning.
We further consider three state-of-the-art (SOTA) offline model-free RL algorithms: CQL [3]}, FisherBRC [4]}, and TD3+BC [5]}; and three SOTA offline MBRL
methods: MOPO [6]}, COMBO [7]}, and WMOPO [8]}.
Experimental details and hyperparameter settings are discussed in Appendix REF .
We run baseline methods using the official implementation under the recommended hyperparameters, except for AlgaeDICE, for which we use the offline version provided by [9]}.
Table REF  shows the mean and standard deviation of the results of AMPL and baselines over five random seeds.
